[Gao et al., (2023)](https://arxiv.org/abs/2211.10435) presents a method that uses LLMs to read natural language problems and generate programs as the intermediate reasoning steps. Coined, program-aided language models (PAL), it differs from chain-of-thought prompting in that instead of using free-form text to obtain solution it offloads the solution step to a programmatic runtime such as a Python interpreter.
In this section, we will cover some examples of multimodal prompting techniques and applications that leverage multiple modalities as opposed to just text alone.
[Liu et al., 2023](https://arxiv.org/abs/2302.08043) introduces GraphPrompt, a new prompting framework for graphs to improve performance on downstream tasks.
